Articles | Volume 12, issue 3
https://doi.org/10.5194/tc-12-907-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-12-907-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
14 Mar 2018
Research article |
| 14 Mar 2018
| 14 Mar 2018
Near-surface temperature inversion during summer at Summit, Greenland, and its relation to MODIS-derived surface temperatures
Thayer School of Engineering, Dartmouth College, Hanover, NH, USA
Department of Physics, St. Olaf College, Northfield, MN, USA
Mary R. Albert
Thayer School of Engineering, Dartmouth College, Hanover, NH, USA
Dorothy K. Hall
Earth System Science Interdisciplinary Center/University of
Maryland, College Park, MD, USA
Cryospheric Sciences Laboratory, NASA Goddard Space Flight Center,
Greenbelt, MD, USA
Related authors
Benjamin Hmiel, Vasilii V. Petrenko, Christo Buizert, Andrew M. Smith, Michael N. Dyonisius, Philip Place, Bin Yang, Quan Hua, Ross Beaudette, Jeffrey P. Severinghaus, Christina Harth, Ray F. Weiss, Lindsey Davidge, Melisa Diaz, Matthew Pacicco, James A. Menking, Michael Kalk, Xavier Faïn, Alden Adolph, Isaac Vimont, and Lee T. Murray
The Cryosphere, 18, 3363–3382, https://doi.org/10.5194/tc-18-3363-2024, https://doi.org/10.5194/tc-18-3363-2024, 2024
Short summary
Short summary
The main aim of this research is to improve understanding of carbon-14 that is produced by cosmic rays in ice sheets. Measurements of carbon-14 in ice cores can provide a range of useful information (age of ice, past atmospheric chemistry, past cosmic ray intensity). Our results show that almost all (>99 %) of carbon-14 that is produced in the upper layer of ice sheets is rapidly lost to the atmosphere. Our results also provide better estimates of carbon-14 production rates in deeper ice.
Adam Schneider, Mark Flanner, Roger De Roo, and Alden Adolph
The Cryosphere, 13, 1753–1766, https://doi.org/10.5194/tc-13-1753-2019, https://doi.org/10.5194/tc-13-1753-2019, 2019
Short summary
Short summary
To study the process of snow aging, we engineered a prototype instrument called the Near-Infrared Emitting and Reflectance-Monitoring Dome (NERD). Using the NERD, we observed rapid snow aging in experiments with added light absorbing particles (LAPs). Particulate matter deposited on the snow increased absorption of solar energy and enhanced snow melt. These results indicate the role of LAPs' indirect effect on snow aging through a positive feedback mechanism related to the snow grain size.
Ingalise Kindstedt, Dominic Winski, C. Max Stevens, Emma Skelton, Luke Copland, Karl Kreutz, Mikaila Mannello, Renée Clavette, Jacob Holmes, Mary Albert, and Scott N. Williamson
EGUsphere, https://doi.org/10.5194/egusphere-2024-3807, https://doi.org/10.5194/egusphere-2024-3807, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
Atmospheric warming over mountain glaciers is leading to increased warming and melting of snow as it compresses into glacier ice. This affects both regional hydrology and climate records contained in the ice. Here we use field observations and modeling to show that surface melting and percolation at Eclipse Icefield (Yukon, Canada) is increasing with an increase in extreme melt events, and that compressing snow at Eclipse is likely to continue warming even if air temperatures remain stable.
Benjamin Hmiel, Vasilii V. Petrenko, Christo Buizert, Andrew M. Smith, Michael N. Dyonisius, Philip Place, Bin Yang, Quan Hua, Ross Beaudette, Jeffrey P. Severinghaus, Christina Harth, Ray F. Weiss, Lindsey Davidge, Melisa Diaz, Matthew Pacicco, James A. Menking, Michael Kalk, Xavier Faïn, Alden Adolph, Isaac Vimont, and Lee T. Murray
The Cryosphere, 18, 3363–3382, https://doi.org/10.5194/tc-18-3363-2024, https://doi.org/10.5194/tc-18-3363-2024, 2024
Short summary
Short summary
The main aim of this research is to improve understanding of carbon-14 that is produced by cosmic rays in ice sheets. Measurements of carbon-14 in ice cores can provide a range of useful information (age of ice, past atmospheric chemistry, past cosmic ray intensity). Our results show that almost all (>99 %) of carbon-14 that is produced in the upper layer of ice sheets is rapidly lost to the atmosphere. Our results also provide better estimates of carbon-14 production rates in deeper ice.
Jason P. Briner, Caleb K. Walcott, Joerg M. Schaefer, Nicolás E. Young, Joseph A. MacGregor, Kristin Poinar, Benjamin A. Keisling, Sridhar Anandakrishnan, Mary R. Albert, Tanner Kuhl, and Grant Boeckmann
The Cryosphere, 16, 3933–3948, https://doi.org/10.5194/tc-16-3933-2022, https://doi.org/10.5194/tc-16-3933-2022, 2022
Short summary
Short summary
The 7.4 m of sea level equivalent stored as Greenland ice is getting smaller every year. The uncertain trajectory of ice loss could be better understood with knowledge of the ice sheet's response to past climate change. Within the bedrock below the present-day ice sheet is an archive of past ice-sheet history. We analyze all available data from Greenland to create maps showing where on the ice sheet scientists can drill, using currently available drills, to obtain sub-ice materials.
Dorothy K. Hall, George A. Riggs, Nicolo E. DiGirolamo, and Miguel O. Román
Hydrol. Earth Syst. Sci., 23, 5227–5241, https://doi.org/10.5194/hess-23-5227-2019, https://doi.org/10.5194/hess-23-5227-2019, 2019
Short summary
Short summary
Global snow cover maps have been available since 2000 from the MODerate resolution Imaging Spectroradiometer (MODIS), and since 2000 and 2011 from the Suomi National Polar-orbiting Partnership (S-NPP) and the Visible Infrared Imaging Radiometer Suite (VIIRS), respectively. These products are used extensively in hydrological modeling and climate studies. New, daily cloud-gap-filled snow products are available from both MODIS and VIIRS, and are being used to develop an Earth science data record.
Adam Schneider, Mark Flanner, Roger De Roo, and Alden Adolph
The Cryosphere, 13, 1753–1766, https://doi.org/10.5194/tc-13-1753-2019, https://doi.org/10.5194/tc-13-1753-2019, 2019
Short summary
Short summary
To study the process of snow aging, we engineered a prototype instrument called the Near-Infrared Emitting and Reflectance-Monitoring Dome (NERD). Using the NERD, we observed rapid snow aging in experiments with added light absorbing particles (LAPs). Particulate matter deposited on the snow increased absorption of solar energy and enhanced snow melt. These results indicate the role of LAPs' indirect effect on snow aging through a positive feedback mechanism related to the snow grain size.
George A. Riggs, Dorothy K. Hall, and Miguel O. Román
Earth Syst. Sci. Data, 9, 765–777, https://doi.org/10.5194/essd-9-765-2017, https://doi.org/10.5194/essd-9-765-2017, 2017
Short summary
Short summary
The current collections of the NASA snow-cover data products generated from the MODIS and Visible Infrared Imaging Radiometer Suite (VIIRS) satellite instruments are described. Revisions in the MODIS snow cover algorithm increased accuracy, and the data content of products is increased from previous collection. The VIIRS algorithm and data product are very similar to those of MODIS. The objective of generating similar products is to enable the creation of a snow-cover extent climate-data record.
K. Keegan, M. R. Albert, and I. Baker
The Cryosphere, 8, 1801–1806, https://doi.org/10.5194/tc-8-1801-2014, https://doi.org/10.5194/tc-8-1801-2014, 2014
A. C. Adolph and M. R. Albert
The Cryosphere, 8, 319–328, https://doi.org/10.5194/tc-8-319-2014, https://doi.org/10.5194/tc-8-319-2014, 2014
S. A. Gregory, M. R. Albert, and I. Baker
The Cryosphere, 8, 91–105, https://doi.org/10.5194/tc-8-91-2014, https://doi.org/10.5194/tc-8-91-2014, 2014
K. Kawamura, J. P. Severinghaus, M. R. Albert, Z. R. Courville, M. A. Fahnestock, T. Scambos, E. Shields, and C. A. Shuman
Atmos. Chem. Phys., 13, 11141–11155, https://doi.org/10.5194/acp-13-11141-2013, https://doi.org/10.5194/acp-13-11141-2013, 2013
H. Fischer, J. Severinghaus, E. Brook, E. Wolff, M. Albert, O. Alemany, R. Arthern, C. Bentley, D. Blankenship, J. Chappellaz, T. Creyts, D. Dahl-Jensen, M. Dinn, M. Frezzotti, S. Fujita, H. Gallee, R. Hindmarsh, D. Hudspeth, G. Jugie, K. Kawamura, V. Lipenkov, H. Miller, R. Mulvaney, F. Parrenin, F. Pattyn, C. Ritz, J. Schwander, D. Steinhage, T. van Ommen, and F. Wilhelms
Clim. Past, 9, 2489–2505, https://doi.org/10.5194/cp-9-2489-2013, https://doi.org/10.5194/cp-9-2489-2013, 2013
Related subject area
Remote Sensing
Five decades of Abramov glacier dynamics reconstructed with multi-sensor optical remote sensing
Updated Arctic melt pond fraction dataset and trends 2002–2023 using ENVISAT and Sentinel-3 remote sensing data
Machine learning of Antarctic firn density by combining radiometer and scatterometer remote-sensing data
Temporal stability of a new 40-year daily AVHRR land surface temperature dataset for the pan-Arctic region
Impact assessment of snow thickness, sea ice density and water density in CryoSat-2-derived sea ice thickness
The Pléiades Glacier Observatory: high-resolution digital elevation models and ortho-imagery to monitor glacier change
Multitemporal UAV lidar detects seasonal heave and subsidence on palsas
Evaluating snow depth retrievals from Sentinel-1 volume scattering over NASA SnowEx sites
Pan-Arctic sea ice concentration from SAR and passive microwave
Land surface temperature trends derived from Landsat imagery in the Swiss Alps
A framework for automated supraglacial lake detection and depth retrieval in ICESat-2 photon data across the Greenland and Antarctic ice sheets
Improved snow property retrievals by solving for topography in the inversion of at-sensor radiance measurements
Change in grounding line location on the Antarctic Peninsula measured using a tidal motion offset correlation method
Land cover succession for recently drained lakes in permafrost on the Yamal Peninsula, Western Siberia
Insights into Greenland Ice Sheet Surface Roughness from Ku-/Ka-band Radar Altimetry Surface Echo Strengths
InSAR-derived seasonal subsidence rates reflect spatial soil moisture patterns in Arctic lowland permafrost regions
Assessing sea ice microwave emissivity up to submillimeter waves from airborne and satellite observations
Simulation of Arctic snow microwave emission in surface-sensitive atmosphere channels
AWI-ICENet1: a convolutional neural network retracker for ice altimetry
Monthly velocity and seasonal variations of the Mont Blanc glaciers derived from Sentinel-2 between 2016 and 2024
Retrieval of snow and soil properties for forward radiative transfer modeling of airborne Ku-band SAR to estimate snow water equivalent: the Trail Valley Creek 2018/19 snow experiment
Evaluating L-band InSAR snow water equivalent retrievals with repeat ground-penetrating radar and terrestrial lidar surveys in northern Colorado
Toward long-term monitoring of regional permafrost thaw with satellite interferometric synthetic aperture radar
Improved records of glacier flow instabilities using customized NASA autoRIFT (CautoRIFT) applied to PlanetScope imagery
Benchmarking passive microwave satellite derived freeze/thaw datasets
Reanalyzing the spatial representativeness of snow depth at automated monitoring stations using airborne lidar data
The AutoICE Challenge
Observing glacier elevation changes from spaceborne optical and radar sensors – an inter-comparison experiment using ASTER and TanDEM-X data
Tower-based C-band radar measurements of an alpine snowpack
A study of sea ice topography in the Weddell and Ross seas using dual-polarimetric TanDEM-X imagery
Estimating differential penetration of green (532 nm) laser light over sea ice with NASA's Airborne Topographic Mapper: observations and models
Mapping surface hoar from near-infrared texture in a laboratory
Sentinel-1 detection of ice slabs on the Greenland Ice Sheet
Estimating the uncertainty of sea-ice area and sea-ice extent from satellite retrievals
Thermal infrared shadow-hiding in GOES-R ABI imagery: snow and forest temperature observations from the SnowEx 2020 Grand Mesa field campaign
Sea ice transport and replenishment across and within the Canadian Arctic Archipelago, 2016–2022
SAR deep learning sea ice retrieval trained with airborne laser scanner measurements from the MOSAiC expedition
Lake ice break-up in Greenland: timing and spatiotemporal variability
Temperature-dominated spatiotemporal variability in snow phenology on the Tibetan Plateau from 2002 to 2022
The pitfalls of ignoring topography in snow retrievals: a case study with EMIT
MMSeaIce: a collection of techniques for improving sea ice mapping with a multi-task model
Snow water equivalent retrieved from X- and dual Ku-band scatterometer measurements at Sodankylä using the Markov Chain Monte Carlo method
Lead fractions from SAR-derived sea ice divergence during MOSAiC
Bayesian physical–statistical retrieval of snow water equivalent and snow depth from X- and Ku-band synthetic aperture radar – demonstration using airborne SnowSAr in SnowEx'17
A low-cost and open-source approach for supraglacial debris thickness mapping using UAV-based infrared thermography
Snow water equivalent retrieval over Idaho – Part 1: Using Sentinel-1 repeat-pass interferometry
Passive microwave remote-sensing-based high-resolution snow depth mapping for Western Himalayan zones using multifactor modeling approach
Refined glacial lake extraction in a high-Asia region by deep neural network and superpixel-based conditional random field methods
Retrieval of snow water equivalent from dual-frequency radar measurements: using time series to overcome the need for accurate a priori information
Inland migration of near-surface crevasses in the Amundsen Sea Sector, West Antarctica
Enrico Mattea, Etienne Berthier, Amaury Dehecq, Tobias Bolch, Atanu Bhattacharya, Sajid Ghuffar, Martina Barandun, and Martin Hoelzle
The Cryosphere, 19, 219–247, https://doi.org/10.5194/tc-19-219-2025, https://doi.org/10.5194/tc-19-219-2025, 2025
Short summary
Short summary
We reconstruct the evolution of terminus position, ice thickness, and surface flow velocity of the reference Abramov glacier (Kyrgyzstan) from 1968 to present. We describe a front pulsation in the early 2000s and the multi-annual present-day buildup of a new pulsation. Such dynamic instabilities can challenge the representativity of Abramov as a reference glacier. For our work we used satellite‑based optical remote sensing from multiple platforms, including recently declassified archives.
Larysa Istomina, Hannah Niehaus, and Gunnar Spreen
The Cryosphere, 19, 83–105, https://doi.org/10.5194/tc-19-83-2025, https://doi.org/10.5194/tc-19-83-2025, 2025
Short summary
Short summary
Melt water puddles, or melt ponds on top of the Arctic sea ice, are a good measure of the Arctic climate state. In the context of recent climate warming, the Arctic has warmed about 4 times faster than the rest of the world, and a long-term dataset of the melt pond fraction is needed to be able to model the future development of the Arctic climate. We present such a dataset, produce 2002–2023 trends and highlight a potential melt regime shift with drastic regional trends of + 20 % per decade.
Weiran Li, Sanne B. M. Veldhuijsen, and Stef Lhermitte
The Cryosphere, 19, 37–61, https://doi.org/10.5194/tc-19-37-2025, https://doi.org/10.5194/tc-19-37-2025, 2025
Short summary
Short summary
This study used a machine learning approach to estimate the densities over the Antarctic Ice Sheet, particularly in the areas where the snow is usually dry. The motivation is to establish a link between satellite parameters to snow densities, as measurements are difficult for people to take on site. It provides valuable insights into the complexities of the relationship between satellite parameters and firn density and provides potential for further studies.
Sonia Dupuis, Frank-Michael Göttsche, and Stefan Wunderle
The Cryosphere, 18, 6027–6059, https://doi.org/10.5194/tc-18-6027-2024, https://doi.org/10.5194/tc-18-6027-2024, 2024
Short summary
Short summary
The Arctic has experienced pronounced warming the last few decades. This warming threatens ecosystems, vegetation dynamics, snow cover duration, and permafrost. Traditional monitoring methods like stations and climate models lack the detail needed. Land surface temperature (LST) data derived from satellites offer high spatial and temporal coverage, perfect for studying changes in the Arctic. In particular, LST information from AVHRR provides a 40-year record, valuable for analysing trends.
Imke Sievers, Henriette Skourup, and Till A. S. Rasmussen
The Cryosphere, 18, 5985–6004, https://doi.org/10.5194/tc-18-5985-2024, https://doi.org/10.5194/tc-18-5985-2024, 2024
Short summary
Short summary
To derive sea ice thickness (SIT) from satellite freeboard (FB) observations, assumptions about snow thickness, snow density, sea ice density and water density are needed. These parameters are impossible to observe alongside FB, so many existing products use empirical values. In this study, modeled values are used instead. The modeled values and otherwise commonly used empirical values are evaluated against in situ observations. In a further analysis, the influence on SIT is quantified.
Etienne Berthier, Jérôme Lebreton, Delphine Fontannaz, Steven Hosford, Joaquín Muñoz-Cobo Belart, Fanny Brun, Liss M. Andreassen, Brian Menounos, and Charlotte Blondel
The Cryosphere, 18, 5551–5571, https://doi.org/10.5194/tc-18-5551-2024, https://doi.org/10.5194/tc-18-5551-2024, 2024
Short summary
Short summary
Repeat elevation measurements are crucial for monitoring glacier health and to understand how glaciers affect river flows and sea level. Until recently, high-resolution elevation data were mostly available for polar regions and High Mountain Asia. Our project, the Pléiades Glacier Observatory, now provides high-resolution topographies of 140 glacier sites worldwide. This is a novel and open dataset to monitor the impact of climate change on glaciers at high resolution and accuracy.
Cas Renette, Mats Olvmo, Sofia Thorsson, Björn Holmer, and Heather Reese
The Cryosphere, 18, 5465–5480, https://doi.org/10.5194/tc-18-5465-2024, https://doi.org/10.5194/tc-18-5465-2024, 2024
Short summary
Short summary
We used a drone to monitor seasonal changes in the height of subarctic permafrost mounds (palsas). With five drone flights in 1 year, we found a seasonal fluctuation of ca. 15 cm as a result of freeze–thaw cycles. On one mound, a large area sank down between each flight as a result of permafrost thaw. The approach of using repeated high-resolution scans from such a drone is unique for such environments and highlights its effectiveness in capturing the subtle dynamics of permafrost landscapes.
Zachary Hoppinen, Ross T. Palomaki, George Brencher, Devon Dunmire, Eric Gagliano, Adrian Marziliano, Jack Tarricone, and Hans-Peter Marshall
The Cryosphere, 18, 5407–5430, https://doi.org/10.5194/tc-18-5407-2024, https://doi.org/10.5194/tc-18-5407-2024, 2024
Short summary
Short summary
This study uses radar imagery from the Sentinel-1 satellite to derive snow depth from increases in the returning energy. These retrieved depths are then compared to nine lidar-derived snow depths across the western United State to assess the ability of this technique to be used to monitor global snow distributions. We also qualitatively compare the changes in underlying Sentinel-1 amplitudes against both the total lidar snow depths and nine automated snow monitoring stations.
Tore Wulf, Jørgen Buus-Hinkler, Suman Singha, Hoyeon Shi, and Matilde Brandt Kreiner
The Cryosphere, 18, 5277–5300, https://doi.org/10.5194/tc-18-5277-2024, https://doi.org/10.5194/tc-18-5277-2024, 2024
Short summary
Short summary
Here, we present ASIP: a new and comprehensive deep-learning-based methodology to retrieve high-resolution sea ice concentration with accompanying well-calibrated uncertainties from satellite-based active and passive microwave observations at a pan-Arctic scale for all seasons. In a comparative study against pan-Arctic ice charts and well-established passive-microwave-based sea ice products, we show that ASIP generalizes well to the pan-Arctic region.
Deniz Tobias Gök, Dirk Scherler, and Hendrik Wulf
The Cryosphere, 18, 5259–5276, https://doi.org/10.5194/tc-18-5259-2024, https://doi.org/10.5194/tc-18-5259-2024, 2024
Short summary
Short summary
We derived Landsat Collection 2 land surface temperature (LST) trends in the Swiss Alps using a harmonic model with a linear trend. Validation with LST data from 119 high-altitude weather stations yielded robust results, but Landsat LST trends are biased due to unstable acquisition times. The bias varies with topographic slope and aspect. We discuss its origin and propose a simple correction method in relation to modeled changes in shortwave radiation.
Philipp Sebastian Arndt and Helen Amanda Fricker
The Cryosphere, 18, 5173–5206, https://doi.org/10.5194/tc-18-5173-2024, https://doi.org/10.5194/tc-18-5173-2024, 2024
Short summary
Short summary
We develop a method for ice-sheet-scale retrieval of supraglacial meltwater depths using ICESat-2 photon data. We report results for two drainage basins in Greenland and Antarctica during two contrasting melt seasons, where our method reveals a total of 1249 lake segments up to 25 m deep. The large volume and wide variety of accurate depth data that our method provides enable the development of data-driven models of meltwater volumes in satellite imagery.
Brenton A. Wilder, Joachim Meyer, Josh Enterkine, and Nancy F. Glenn
The Cryosphere, 18, 5015–5029, https://doi.org/10.5194/tc-18-5015-2024, https://doi.org/10.5194/tc-18-5015-2024, 2024
Short summary
Short summary
Remotely sensed properties of snow are dependent on accurate terrain information, which for a lot of the cryosphere and seasonal snow zones is often insufficient in accuracy. However, as we show in this paper, we can bypass this issue by optimally solving for the terrain by utilizing the raw radiance data returned to the sensor. This method performed well when compared to validation datasets and has the potential to be used across a variety of different snow climates.
Benjamin J. Wallis, Anna E. Hogg, Yikai Zhu, and Andrew Hooper
The Cryosphere, 18, 4723–4742, https://doi.org/10.5194/tc-18-4723-2024, https://doi.org/10.5194/tc-18-4723-2024, 2024
Short summary
Short summary
The grounding line, where ice begins to float, is an essential variable to understand ice dynamics, but in some locations it can be challenging to measure with established techniques. Using satellite data and a new method, Wallis et al. measure the grounding line position of glaciers and ice shelves in the Antarctic Peninsula and find retreats of up to 16.3 km have occurred since the last time measurements were made in the 1990s.
Clemens von Baeckmann, Annett Bartsch, Helena Bergstedt, Aleksandra Efimova, Barbara Widhalm, Dorothee Ehrich, Timo Kumpula, Alexander Sokolov, and Svetlana Abdulmanova
The Cryosphere, 18, 4703–4722, https://doi.org/10.5194/tc-18-4703-2024, https://doi.org/10.5194/tc-18-4703-2024, 2024
Short summary
Short summary
Lakes are common features in Arctic permafrost areas. Land cover change following their drainage needs to be monitored since it has implications for ecology and the carbon cycle. Satellite data are key in this context. We compared a common vegetation index approach with a novel land-cover-monitoring scheme. Land cover information provides specific information on wetland features. We also showed that the bioclimatic gradients play a significant role after drainage within the first 10 years.
Kirk M. Scanlan, Anja Rutishauser, and Sebastian B. Simonsen
EGUsphere, https://doi.org/10.5194/egusphere-2024-2832, https://doi.org/10.5194/egusphere-2024-2832, 2024
Short summary
Short summary
In this paper we investigate how to interpret Greenland surface roughness derived from reflected radar altimetry signals. Based on a comparison to conventional laser altimetry results, we 1) define a new mapping between the radar surface echo power strengths and surface roughness and 2) contextualize the horizontal lengths over which this roughness is representative. This work provides critical insight into how these observations integrate into Greenland Ice Sheet mass balance modeling.
Barbara Widhalm, Annett Bartsch, Tazio Strozzi, Nina Jones, Artem Khomutov, Elena Babkina, Marina Leibman, Rustam Khairullin, Mathias Göckede, Helena Bergstedt, Clemens von Baeckmann, and Xaver Muri
EGUsphere, https://doi.org/10.5194/egusphere-2024-2356, https://doi.org/10.5194/egusphere-2024-2356, 2024
Short summary
Short summary
Mapping soil moisture in Arctic permafrost regions is crucial for various activities, but it is challenging with typical satellite methods due to the landscape's diversity. Seasonal freezing and thawing cause the ground to periodically rise and subside. Our research demonstrates that this seasonal ground settlement, measured with Sentinel-1 satellite data, is larger in areas with wetter soils. This method helps to monitor permafrost degradation.
Nils Risse, Mario Mech, Catherine Prigent, Gunnar Spreen, and Susanne Crewell
The Cryosphere, 18, 4137–4163, https://doi.org/10.5194/tc-18-4137-2024, https://doi.org/10.5194/tc-18-4137-2024, 2024
Short summary
Short summary
Passive microwave observations from satellites are crucial for monitoring Arctic sea ice and atmosphere. To do this effectively, it is important to understand how sea ice emits microwaves. Through unique Arctic sea ice observations, we improved our understanding, identified four distinct emission types, and expanded current knowledge to include higher frequencies. These findings will enhance our ability to monitor the Arctic climate and provide valuable information for new satellite missions.
Melody Sandells, Nick Rutter, Kirsty Wivell, Richard Essery, Stuart Fox, Chawn Harlow, Ghislain Picard, Alexandre Roy, Alain Royer, and Peter Toose
The Cryosphere, 18, 3971–3990, https://doi.org/10.5194/tc-18-3971-2024, https://doi.org/10.5194/tc-18-3971-2024, 2024
Short summary
Short summary
Satellite microwave observations are used for weather forecasting. In Arctic regions this is complicated by natural emission from snow. By simulating airborne observations from in situ measurements of snow, this study shows how snow properties affect the signal within the atmosphere. Fresh snowfall between flights changed airborne measurements. Good knowledge of snow layering and structure can be used to account for the effects of snow and could unlock these data to improve forecasts.
Veit Helm, Alireza Dehghanpour, Ronny Hänsch, Erik Loebel, Martin Horwath, and Angelika Humbert
The Cryosphere, 18, 3933–3970, https://doi.org/10.5194/tc-18-3933-2024, https://doi.org/10.5194/tc-18-3933-2024, 2024
Short summary
Short summary
We present a new approach (AWI-ICENet1), based on a deep convolutional neural network, for analysing satellite radar altimeter measurements to accurately determine the surface height of ice sheets. Surface height estimates obtained with AWI-ICENet1 (along with related products, such as ice sheet height change and volume change) show improved and unbiased results compared to other products. This is important for the long-term monitoring of ice sheet mass loss and its impact on sea level rise.
Fabrizio Troilo, Niccolò Dematteis, Francesco Zucca, Martin Funk, and Daniele Giordan
The Cryosphere, 18, 3891–3909, https://doi.org/10.5194/tc-18-3891-2024, https://doi.org/10.5194/tc-18-3891-2024, 2024
Short summary
Short summary
The study of glacier sliding along slopes is relevant in many aspects of glaciology. We processed Sentinel-2 satellite optical images of Mont Blanc, obtaining surface velocities of 30 glaciers between 2016 and 2024. The study revealed different behaviours and velocity variations that have relationships with glacier morphology. A velocity anomaly was observed in some glaciers of the southern side in 2020–2022, but its origin needs to be investigated further.
Benoit Montpetit, Joshua King, Julien Meloche, Chris Derksen, Paul Siqueira, J. Max Adam, Peter Toose, Mike Brady, Anna Wendleder, Vincent Vionnet, and Nicolas R. Leroux
The Cryosphere, 18, 3857–3874, https://doi.org/10.5194/tc-18-3857-2024, https://doi.org/10.5194/tc-18-3857-2024, 2024
Short summary
Short summary
This paper validates the use of free open-source models to link distributed snow measurements to radar measurements in the Canadian Arctic. Using multiple radar sensors, we can decouple the soil from the snow contribution. We then retrieve the "microwave snow grain size" to characterize the interaction between the snow mass and the radar signal. This work supports future satellite mission development to retrieve snow mass information such as the future Canadian Terrestrial Snow Mass Mission.
Randall Bonnell, Daniel McGrath, Jack Tarricone, Hans-Peter Marshall, Ella Bump, Caroline Duncan, Stephanie Kampf, Yunling Lou, Alex Olsen-Mikitowicz, Megan Sears, Keith Williams, Lucas Zeller, and Yang Zheng
The Cryosphere, 18, 3765–3785, https://doi.org/10.5194/tc-18-3765-2024, https://doi.org/10.5194/tc-18-3765-2024, 2024
Short summary
Short summary
Snow provides water for billions of people, but the amount of snow is difficult to detect remotely. During the 2020 and 2021 winters, a radar was flown over mountains in Colorado, USA, to measure the amount of snow on the ground, while our team collected ground observations to test the radar technique’s capabilities. The technique yielded accurate measurements of the snowpack that had good correlation with ground measurements, making it a promising application for the upcoming NISAR satellite.
Taha Sadeghi Chorsi, Franz J. Meyer, and Timothy H. Dixon
The Cryosphere, 18, 3723–3740, https://doi.org/10.5194/tc-18-3723-2024, https://doi.org/10.5194/tc-18-3723-2024, 2024
Short summary
Short summary
The active layer thaws and freezes seasonally. The annual freeze–thaw cycle of the active layer causes significant surface height changes due to the volume difference between ice and liquid water. We estimate the subsidence rate and active-layer thickness (ALT) for part of northern Alaska for summer 2017 to 2022 using interferometric synthetic aperture radar and lidar. ALT estimates range from ~20 cm to larger than 150 cm in area. Subsidence rate varies between close points (2–18 mm per month).
Jukes Liu, Madeline Gendreau, Ellyn Mary Enderlin, and Rainey Aberle
The Cryosphere, 18, 3571–3590, https://doi.org/10.5194/tc-18-3571-2024, https://doi.org/10.5194/tc-18-3571-2024, 2024
Short summary
Short summary
There are sometimes gaps in global glacier velocity records produced using satellite image feature-tracking algorithms during times of rapid glacier acceleration, which hinders the study of glacier flow processes. We present an open-source pipeline for customizing the feature-tracking parameters and for including images from an additional source. We applied it to five glaciers and found that it produced accurate velocity data that supplemented their velocity records during rapid acceleration.
Annett Bartsch, Xaver Muri, Markus Hetzenecker, Kimmo Rautiainen, Helena Bergstedt, Jan Wuite, Thomas Nagler, and Dmitry Nicolsky
EGUsphere, https://doi.org/10.5194/egusphere-2024-2518, https://doi.org/10.5194/egusphere-2024-2518, 2024
Short summary
Short summary
We developed a robust freeze/thaw detection approach, applying a constant threshold on Copernicus Sentinel-1 data, that is suitable for tundra regions. All global, coarser resolution products, tested with the resulting benchmarking dataset, are of value for freeze/thaw retrieval, although differences were found depending on seasons, in particular during spring and autumn transition.
Jordan N. Herbert, Mark S. Raleigh, and Eric E. Small
The Cryosphere, 18, 3495–3512, https://doi.org/10.5194/tc-18-3495-2024, https://doi.org/10.5194/tc-18-3495-2024, 2024
Short summary
Short summary
Automated stations measure snow properties at a single point but are frequently used to validate data that represent much larger areas. We use lidar snow depth data to see how often the mean snow depth surrounding a snow station is within 10 cm of the snow station depth at different scales. We found snow stations overrepresent the area-mean snow depth in ~ 50 % of cases, but the direction of bias at a site is temporally consistent, suggesting a site could be calibrated to the surrounding area.
Andreas Stokholm, Jørgen Buus-Hinkler, Tore Wulf, Anton Korosov, Roberto Saldo, Leif Toudal Pedersen, David Arthurs, Ionut Dragan, Iacopo Modica, Juan Pedro, Annekatrien Debien, Xinwei Chen, Muhammed Patel, Fernando Jose Pena Cantu, Javier Noa Turnes, Jinman Park, Linlin Xu, Katharine Andrea Scott, David Anthony Clausi, Yuan Fang, Mingzhe Jiang, Saeid Taleghanidoozdoozan, Neil Curtis Brubacher, Armina Soleymani, Zacharie Gousseau, Michał Smaczny, Patryk Kowalski, Jacek Komorowski, David Rijlaarsdam, Jan Nicolaas van Rijn, Jens Jakobsen, Martin Samuel James Rogers, Nick Hughes, Tom Zagon, Rune Solberg, Nicolas Longépé, and Matilde Brandt Kreiner
The Cryosphere, 18, 3471–3494, https://doi.org/10.5194/tc-18-3471-2024, https://doi.org/10.5194/tc-18-3471-2024, 2024
Short summary
Short summary
The AutoICE challenge encouraged the development of deep learning models to map multiple aspects of sea ice – the amount of sea ice in an area and the age and ice floe size – using multiple sources of satellite and weather data across the Canadian and Greenlandic Arctic. Professionally drawn operational sea ice charts were used as a reference. A total of 179 students and sea ice and AI specialists participated and produced maps in broad agreement with the sea ice charts.
Livia Piermattei, Michael Zemp, Christian Sommer, Fanny Brun, Matthias H. Braun, Liss M. Andreassen, Joaquín M. C. Belart, Etienne Berthier, Atanu Bhattacharya, Laura Boehm Vock, Tobias Bolch, Amaury Dehecq, Inés Dussaillant, Daniel Falaschi, Caitlyn Florentine, Dana Floricioiu, Christian Ginzler, Gregoire Guillet, Romain Hugonnet, Matthias Huss, Andreas Kääb, Owen King, Christoph Klug, Friedrich Knuth, Lukas Krieger, Jeff La Frenierre, Robert McNabb, Christopher McNeil, Rainer Prinz, Louis Sass, Thorsten Seehaus, David Shean, Désirée Treichler, Anja Wendt, and Ruitang Yang
The Cryosphere, 18, 3195–3230, https://doi.org/10.5194/tc-18-3195-2024, https://doi.org/10.5194/tc-18-3195-2024, 2024
Short summary
Short summary
Satellites have made it possible to observe glacier elevation changes from all around the world. In the present study, we compared the results produced from two different types of satellite data between different research groups and against validation measurements from aeroplanes. We found a large spread between individual results but showed that the group ensemble can be used to reliably estimate glacier elevation changes and related errors from satellite data.
Isis Brangers, Hans-Peter Marshall, Gabrielle De Lannoy, Devon Dunmire, Christian Mätzler, and Hans Lievens
The Cryosphere, 18, 3177–3193, https://doi.org/10.5194/tc-18-3177-2024, https://doi.org/10.5194/tc-18-3177-2024, 2024
Short summary
Short summary
To better understand the interactions between C-band radar waves and snow, a tower-based experiment was set up in the Idaho Rocky Mountains. The reflections were collected in the time domain to measure the backscatter profile from the various snowpack and ground surface layers. The results demonstrate that C-band radar is sensitive to seasonal patterns in snow accumulation but that changes in microstructure, stratigraphy and snow wetness may complicate satellite-based snow depth retrievals.
Lanqing Huang and Irena Hajnsek
The Cryosphere, 18, 3117–3140, https://doi.org/10.5194/tc-18-3117-2024, https://doi.org/10.5194/tc-18-3117-2024, 2024
Short summary
Short summary
Interferometric synthetic aperture radar can measure the total freeboard of sea ice but can be biased when radar signals penetrate snow and ice. We develop a new method to retrieve the total freeboard and analyze the regional variation of total freeboard and roughness in the Weddell and Ross seas. We also investigate the statistical behavior of the total freeboard for diverse ice types. The findings enhance the understanding of Antarctic sea ice topography and its dynamics in a changing climate.
Michael Studinger, Benjamin E. Smith, Nathan Kurtz, Alek Petty, Tyler Sutterley, and Rachel Tilling
The Cryosphere, 18, 2625–2652, https://doi.org/10.5194/tc-18-2625-2024, https://doi.org/10.5194/tc-18-2625-2024, 2024
Short summary
Short summary
We use green lidar data and natural-color imagery over sea ice to quantify elevation biases potentially impacting estimates of change in ice thickness of the polar regions. We complement our analysis using a model of scattering of light in snow and ice that predicts the shape of lidar waveforms reflecting from snow and ice surfaces based on the shape of the transmitted pulse. We find that biased elevations exist in airborne and spaceborne data products from green lidars.
James Dillon, Christopher Donahue, Evan Schehrer, Karl Birkeland, and Kevin Hammonds
The Cryosphere, 18, 2557–2582, https://doi.org/10.5194/tc-18-2557-2024, https://doi.org/10.5194/tc-18-2557-2024, 2024
Short summary
Short summary
Surface hoar crystals are snow grains that form when vapor deposits on a snow surface. They create a weak layer in the snowpack that can cause large avalanches to occur. Thus, determining when and where surface hoar forms is a lifesaving matter. Here, we developed a means of mapping surface hoar using remote-sensing technologies. We found that surface hoar displayed heightened texture, hence the variability of brightness. Using this, we created surface hoar maps with an accuracy upwards of 95 %.
Riley Culberg, Roger J. Michaelides, and Julie Z. Miller
The Cryosphere, 18, 2531–2555, https://doi.org/10.5194/tc-18-2531-2024, https://doi.org/10.5194/tc-18-2531-2024, 2024
Short summary
Short summary
Ice slabs enhance meltwater runoff from the Greenland Ice Sheet. Therefore, it is important to understand their extent and change in extent over time. We present a new method for detecting ice slabs in satellite radar data, which we use to map ice slabs at 500 m resolution across the entire ice sheet in winter 2016–2017. Our results provide better spatial coverage and resolution than previous maps from airborne radar and lay the groundwork for long-term monitoring of ice slabs from space.
Andreas Wernecke, Dirk Notz, Stefan Kern, and Thomas Lavergne
The Cryosphere, 18, 2473–2486, https://doi.org/10.5194/tc-18-2473-2024, https://doi.org/10.5194/tc-18-2473-2024, 2024
Short summary
Short summary
The total Arctic sea-ice area (SIA), which is an important climate indicator, is routinely monitored with the help of satellite measurements. Uncertainties in observations of sea-ice concentration (SIC) partly cancel out when summed up to the total SIA, but the degree to which this is happening has been unclear. Here we find that the uncertainty daily SIA estimates, based on uncertainties in SIC, are about 300 000 km2. The 2002 to 2017 September decline in SIA is approx. 105 000 ± 9000 km2 a−1.
Steven J. Pestana, C. Chris Chickadel, and Jessica D. Lundquist
The Cryosphere, 18, 2257–2276, https://doi.org/10.5194/tc-18-2257-2024, https://doi.org/10.5194/tc-18-2257-2024, 2024
Short summary
Short summary
We compared infrared images taken by GOES-R satellites of an area with snow and forests against surface temperature measurements taken on the ground, from an aircraft, and by another satellite. We found that GOES-R measured warmer temperatures than the other measurements, especially in areas with more forest and when the Sun was behind the satellite. From this work, we learned that the position of the Sun and surface features such as trees that can cast shadows impact GOES-R infrared images.
Stephen E. L. Howell, David G. Babb, Jack C. Landy, Isolde A. Glissenaar, Kaitlin McNeil, Benoit Montpetit, and Mike Brady
The Cryosphere, 18, 2321–2333, https://doi.org/10.5194/tc-18-2321-2024, https://doi.org/10.5194/tc-18-2321-2024, 2024
Short summary
Short summary
The CAA serves as both a source and a sink for sea ice from the Arctic Ocean, while also exporting sea ice into Baffin Bay. It is also an important region with respect to navigating the Northwest Passage. Here, we quantify sea ice transport and replenishment across and within the CAA from 2016 to 2022. We also provide the first estimates of the ice area and volume flux within the CAA from the Queen Elizabeth Islands to Parry Channel, which spans the central region of the Northwest Passage.
Karl Kortum, Suman Singha, Gunnar Spreen, Nils Hutter, Arttu Jutila, and Christian Haas
The Cryosphere, 18, 2207–2222, https://doi.org/10.5194/tc-18-2207-2024, https://doi.org/10.5194/tc-18-2207-2024, 2024
Short summary
Short summary
A dataset of 20 radar satellite acquisitions and near-simultaneous helicopter-based surveys of the ice topography during the MOSAiC expedition is constructed and used to train a variety of deep learning algorithms. The results give realistic insights into the accuracy of retrieval of measured ice classes using modern deep learning models. The models able to learn from the spatial distribution of the measured sea ice classes are shown to have a clear advantage over those that cannot.
Christoph Posch, Jakob Abermann, and Tiago Silva
The Cryosphere, 18, 2035–2059, https://doi.org/10.5194/tc-18-2035-2024, https://doi.org/10.5194/tc-18-2035-2024, 2024
Short summary
Short summary
Radar beams from satellites exhibit reflection differences between water and ice. This condition, as well as the comprehensive coverage and high temporal resolution of the Sentinel-1 satellites, allows automatically detecting the timing of when ice cover of lakes in Greenland disappear. We found that lake ice breaks up 3 d later per 100 m elevation gain and that the average break-up timing varies by ±8 d in 2017–2021, which has major implications for the energy budget of the lakes.
Jiahui Xu, Yao Tang, Linxin Dong, Shujie Wang, Bailang Yu, Jianping Wu, Zhaojun Zheng, and Yan Huang
The Cryosphere, 18, 1817–1834, https://doi.org/10.5194/tc-18-1817-2024, https://doi.org/10.5194/tc-18-1817-2024, 2024
Short summary
Short summary
Understanding snow phenology (SP) and its possible feedback are important. We reveal spatiotemporal heterogeneous SP on the Tibetan Plateau (TP) and the mediating effects from meteorological, topographic, and environmental factors on it. The direct effects of meteorology on SP are much greater than the indirect effects. Topography indirectly effects SP, while vegetation directly effects SP. This study contributes to understanding past global warming and predicting future trends on the TP.
Niklas Bohn, Edward H. Bair, Philip G. Brodrick, Nimrod Carmon, Robert O. Green, Thomas H. Painter, and David R. Thompson
EGUsphere, https://doi.org/10.2139/ssrn.4671920, https://doi.org/10.2139/ssrn.4671920, 2024
Short summary
Short summary
A new type of Earth-observing satellite is measuring reflected sunlight in all its colors. These measurements can be used to characterize snow properties, which give us important information about climate change. In our work, we emphasize the difficulties of obtaining these properties from rough mountainous regions and present a solution to the problem. Our research was inspired by the growing number of new satellite technologies and the increasing challenges associated with climate change.
Xinwei Chen, Muhammed Patel, Fernando J. Pena Cantu, Jinman Park, Javier Noa Turnes, Linlin Xu, K. Andrea Scott, and David A. Clausi
The Cryosphere, 18, 1621–1632, https://doi.org/10.5194/tc-18-1621-2024, https://doi.org/10.5194/tc-18-1621-2024, 2024
Short summary
Short summary
This paper introduces an automated sea ice mapping pipeline utilizing a multi-task U-Net architecture. It attained the top score of 86.3 % in the AutoICE challenge. Ablation studies revealed that incorporating brightness temperature data and spatial–temporal information significantly enhanced model accuracy. Accurate sea ice mapping is vital for comprehending the Arctic environment and its global climate effects, underscoring the potential of deep learning.
Jinmei Pan, Michael Durand, Juha Lemmetyinen, Desheng Liu, and Jiancheng Shi
The Cryosphere, 18, 1561–1578, https://doi.org/10.5194/tc-18-1561-2024, https://doi.org/10.5194/tc-18-1561-2024, 2024
Short summary
Short summary
We developed an algorithm to estimate snow mass using X- and dual Ku-band radar, and tested it in a ground-based experiment. The algorithm, the Bayesian-based Algorithm for SWE Estimation (BASE) using active microwaves, achieved an RMSE of 30 mm for snow water equivalent. These results demonstrate the potential of radar, a highly promising sensor, to map snow mass at high spatial resolution.
Luisa von Albedyll, Stefan Hendricks, Nils Hutter, Dmitrii Murashkin, Lars Kaleschke, Sascha Willmes, Linda Thielke, Xiangshan Tian-Kunze, Gunnar Spreen, and Christian Haas
The Cryosphere, 18, 1259–1285, https://doi.org/10.5194/tc-18-1259-2024, https://doi.org/10.5194/tc-18-1259-2024, 2024
Short summary
Short summary
Leads (openings in sea ice cover) are created by sea ice dynamics. Because they are important for many processes in the Arctic winter climate, we aim to detect them with satellites. We present two new techniques to detect lead widths of a few hundred meters at high spatial resolution (700 m) and independent of clouds or sun illumination. We use the MOSAiC drift 2019–2020 in the Arctic for our case study and compare our new products to other existing lead products.
Siddharth Singh, Michael Durand, Edward Kim, and Ana P. Barros
The Cryosphere, 18, 747–773, https://doi.org/10.5194/tc-18-747-2024, https://doi.org/10.5194/tc-18-747-2024, 2024
Short summary
Short summary
Seasonal snowfall accumulation plays a critical role in climate. The water stored in it is measured by the snow water equivalent (SWE), the amount of water released after completely melting. We demonstrate a Bayesian physical–statistical framework to estimate SWE from airborne X- and Ku-band synthetic aperture radar backscatter measurements constrained by physical snow hydrology and radar models. We explored spatial resolutions and vertical structures that agree well with ground observations.
Jérôme Messmer and Alexander Raphael Groos
The Cryosphere, 18, 719–746, https://doi.org/10.5194/tc-18-719-2024, https://doi.org/10.5194/tc-18-719-2024, 2024
Short summary
Short summary
The lower part of mountain glaciers is often covered with debris. Knowing the thickness of the debris is important as it influences the melting and future evolution of the affected glaciers. We have developed an open-source approach to map variations in debris thickness on glaciers using a low-cost drone equipped with a thermal infrared camera. The resulting high-resolution maps of debris surface temperature and thickness enable more accurate monitoring and modelling of debris-covered glaciers.
Shadi Oveisgharan, Robert Zinke, Zachary Hoppinen, and Hans Peter Marshall
The Cryosphere, 18, 559–574, https://doi.org/10.5194/tc-18-559-2024, https://doi.org/10.5194/tc-18-559-2024, 2024
Short summary
Short summary
The seasonal snowpack provides water resources to billions of people worldwide. Large-scale mapping of snow water equivalent (SWE) with high resolution is critical for many scientific and economics fields. In this work we used the radar remote sensing interferometric synthetic aperture radar (InSAR) to estimate the SWE change between 2 d. The error in the estimated SWE change is less than 2 cm for in situ stations. Additionally, the retrieved SWE using InSAR is correlated with lidar snow depth.
Dhiraj Kumar Singh, Srinivasarao Tanniru, Kamal Kant Singh, Harendra Singh Negi, and RAAJ Ramsankaran
The Cryosphere, 18, 451–474, https://doi.org/10.5194/tc-18-451-2024, https://doi.org/10.5194/tc-18-451-2024, 2024
Short summary
Short summary
In situ techniques for snow depth (SD) measurement are not adequate to represent the spatiotemporal variability in SD in the Western Himalayan region. Therefore, this study focuses on the high-resolution mapping of daily snow depth in the Indian Western Himalayan region using passive microwave remote-sensing-based algorithms. Overall, the proposed multifactor SD models demonstrated substantial improvement compared to the operational products. However, there is a scope for further improvement.
Yungang Cao, Rumeng Pan, Meng Pan, Ruodan Lei, Puying Du, and Xueqin Bai
The Cryosphere, 18, 153–168, https://doi.org/10.5194/tc-18-153-2024, https://doi.org/10.5194/tc-18-153-2024, 2024
Short summary
Short summary
This study built a glacial lake dataset with 15376 samples in seven types and proposed an automatic method by two-stage (the semantic segmentation network and post-processing) optimizations to detect glacial lakes. The proposed method for glacial lake extraction has achieved the best results so far, in which the F1 score and IoU reached 0.945 and 0.907, respectively. The area of the minimum glacial lake that can be entirely and correctly extracted has been raised to the 100 m2 level.
Michael Durand, Joel T. Johnson, Jack Dechow, Leung Tsang, Firoz Borah, and Edward J. Kim
The Cryosphere, 18, 139–152, https://doi.org/10.5194/tc-18-139-2024, https://doi.org/10.5194/tc-18-139-2024, 2024
Short summary
Short summary
Seasonal snow accumulates each winter, storing water to release later in the year and modulating both water and energy cycles, but the amount of seasonal snow is one of the most poorly measured components of the global water cycle. Satellite concepts to monitor snow accumulation have been proposed but not selected. This paper shows that snow accumulation can be measured using radar, and that (contrary to previous studies) does not require highly accurate information about snow microstructure.
Andrew O. Hoffman, Knut Christianson, Ching-Yao Lai, Ian Joughin, Nicholas Holschuh, Elizabeth Case, Jonathan Kingslake, and the GHOST science team
EGUsphere, https://doi.org/10.5194/egusphere-2023-2956, https://doi.org/10.5194/egusphere-2023-2956, 2024
Short summary
Short summary
We use satellite and ice-penetrating radar technology to segment crevasses in the Amundsen Sea Embayment. Inspection of satellite time series reveals inland expansion of crevasses where surface stresses have increased. We develop a simple model for the strength of densifying snow and show that these crevasses are likely restricted to the near surface. This result bridges discrepancies between satellite and lab experiments and reveals the importance of porosity on surface crevasse formation.
Cited articles
Adolph, A., Albert, M., and Hall, D.: Infrared Surface Temperature near
Summit, Greenland in June and July of 2015, Arctic Data Center,
https://doi.org/10.18739/A2W27K, 2018.
Ahlstrøm, A. P. and PROMICE project team: A new programme for monitoring
the mass loss of the Greenland ice sheet, in: Review of survey activities 2007,
edited by: Bennike, O. and Higgins, A. K., GEUS, Copenhagen (Geological Survey of
Denmark and Greenland Bulletin 15), 2008.
ARM Climate Research Facility: Millimeter Wavelength Cloud Radar (MMCRMOM),
updated hourly, 1 June 2015–30 July 2018, Summit Station, Greenland (SMT)
External Data (satellites and others) (X1), compiled by: Johnson, K. and
Bharadwaj, N., Atmospheric Radiation Measurement (ARM) Climate Research
Facility Data Archive, Oak Ridge, Tennessee, USA, https://doi.org/10.5439/1025228 (last
access: 7 March 2018), 2010.
Berkelhammer, M., Noone, D. C., Steen-Larsen, H. C., Bailey, A., Cox, C. J.,
O'Neill, M. S., Schneider, D., Steffen, K., and White, J. W. C.:
Surface-atmosphere decoupling limits accumulation at Summit, Greenland, Sci.
Adv., 2, e1501704, https://doi.org/10.1126/sciadv.1501704, 2016.
Box, J. E.: Greenland ice sheet surface mass balance reconstruction. Part
II: surface mass balance (1840–2010), J. Climate, 26, 6974–6989,
https://doi.org/10.1175/JCLI-D-12-00518.1, 2013.
Casey, K. A., Polashenski, C. M., Chen, J., and Tedesco, M.: Impact of MODIS
sensor calibration updates on Greenland Ice Sheet surface reflectance and
albedo trends, The Cryosphere, 11, 1781–1795,
https://doi.org/10.5194/tc-11-1781-2017, 2017.
Comiso, J. C.: Warming trends in the Arctic from clear sky satellite
observations, J. Climate, 16, 3498–3510,
https://doi.org/10.1175/1520-0442(2003)016<3498:WTITAF>2.0.CO;2, 2003.
Fausto, R., van As, D., and Ahlstrøm, A.: Assessing the accuracy of
Greenland ice sheet ice ablation measurements by pressure transducer, J.
Glaciol., 58, 1144–1150, https://doi.org/10.3189/2012JoG12J075, 2012.
Fréville, H., Brun, E., Picard, G., Tatarinova, N., Arnaud, L., Lanconelli,
C., Reijmer, C., and van den Broeke, M.: Using MODIS land surface
temperatures and the Crocus snow model to understand the warm bias of
ERA-Interim reanalyses at the surface in Antarctica, The Cryosphere, 8,
1361–1373, https://doi.org/10.5194/tc-8-1361-2014, 2014.
Good, E. J.: An in situ-based analysis of the relationship between land
surface “skin” and screen-level air temperatures, J. Geophys. Res.-Atmos.,
121, 8801–8819, https://doi.org/10.1002/2016JD025318, 2016.
Guillevic, P., Göttsche, F., Nickeson, J., Hulley, G., Ghent, D., Yu, Y.,
Trigo, I., Hook, S., Sobrino, J. A., Remedios, J., Román, M., and
Camacho, F.: Land Surface Temperature Product Validation Best Practice
Protocol. Version 1.0, edited by: Guillevic, P., Göttsche, F., Nickeson,
J., and Román, M., Best Practice for Satellite-Derived Land Product
Validation (p. 60): Land Product Validation Subgroup (WGCV/CEOS),
https://doi.org/10.5067/doc/ceoswgcv/lpv/lst.001, 2018.
Hachem, S., Duguay, C. R., and Allard, M.: Comparison of MODIS-derived land
surface temperatures with ground surface and air temperature measurements in
continuous permafrost terrain, The Cryosphere, 6, 51–69,
https://doi.org/10.5194/tc-6-51-2012, 2012.
Hall, D. K., Key, J. R., Casey, K. A., Riggs, G. A., and Cavalieri, D. J.:
Sea ice surface temperature product from MODIS, IEEE T. Geosci. Remote, 42,
1076–1087, https://doi.org/10.1109/TGRS.2004.825587, 2004.
Hall, D. K., Box, J. E., Casey, K. A., Hook, S. J., Shuman, C. A., and
Steffen, K.: Comparison of satellite-derived and in situ observations of ice
and snow surface temperatures over Greenland, Remote Sens. Environ., 112,
3739–3749, https://doi.org/10.1016/j.rse.2008.05.007, 2008.
Hall, D. K., Comiso, J. C., DiGirolamo, N. E., Shuman, C. A., Key, J. R., and
Koenig, L. S.: A satellite-derived climate-quality data record of the
clear-sky surface temperature of the Greenland ice sheet, J. Climate, 25,
4785–4798, https://doi.org/10.1175/JCLI-D-11-00365.1, 2012.
Hall, D. K., Comiso, J. C., DiGirolamo, N. E., Shuman, C. A., Box, J. E., and
Koenig, L. S.: Variability in the surface temperature and melt extent of the
Greenland ice sheet from MODIS, Geophys. Res. Lett., 40, 2114–2120,
https://doi.org/10.1002/grl.50240, 2013.
Hall, D. K., Nghiem, S. V., Rigor, I. G., and Miller, J. A.: Uncertainties of
temperature measurements on snow-covered land and sea ice from in situ and
MODIS data during BROMEX, J. Appl. Meteorol. Climatol., 54, 966–978,
https://doi.org/10.1175/JAMC-D-14-0175.1, 2015.
Hook, S. J., Vaughan, R. G., Tonooka, H., and Schladow, S. G.: Absolute
Radiometric In-Flight Validation of Mid Infrared and Thermal Infrared Data
From ASTER and MODIS on the Terra Spacecraft Using the Lake Tahoe, CA/NV,
USA, Automated Validation Site, IEEE T. Geosci. Remote, 45, 1798–1807,
https://doi.org/10.1109/TGRS.2007.894564, 2007.
Hudson, S. R. and Brandt, R. E.: A look at the surface-based temperature
inversion on the Antarctic Plateau, J. Climate, 18, 1673–1696,
https://doi.org/10.1175/JCLI3360.1, 2005.
Kahl, J. D.: Characteristics of the low-level temperature inversion along the
Alaskan Arctic coast, Int. J. Climatol., 10, 537–548,
https://doi.org/10.1002/joc.3370100509, 1990.
Koenig, L. S. and Hall, D. K.: Comparison of satellite, thermochron and air
temperatures at Summit, Greenland, during the winter of 2008/09, J. Glaciol.,
56, 735–741, https://doi.org/10.3189/002214310793146269, 2010.
Kuipers Munneke, P., van den Broeke, M. R., King, J. C., Gray, T., and
Reijmer, C. H.: Near-surface climate and surface energy budget of Larsen C
ice shelf, Antarctic Peninsula, The Cryosphere, 6, 353–363,
https://doi.org/10.5194/tc-6-353-2012, 2012.
Lyapustin, A., Wang, Y., Xiong, X., Meister, G., Platnick, S., Levy, R.,
Franz, B., Korkin, S., Hilker, T., Tucker, J., Hall, F., Sellers, P., Wu, A.,
and Angal, A.: Scientific impact of MODIS C5 calibration degradation and C6+
improvements, Atmos. Meas. Tech., 7, 4353–4365,
https://doi.org/10.5194/amt-7-4353-2014, 2014.
Miller, N. B., Turner, D. D., Bennartz, R., Shupe, M. D., Kulie, M. S.,
Cadeddu, M. P., and Walden, V. P.: Surface-based inversions above central
Greenland, J. Geophys. Res.-Atmos., 118, 495–506, https://doi.org/10.1029/2012JD018867,
2013.
Miller, N. B., Shupe, M. D., Cox, C. J., Walden, V. P., Turner, D. D., and
Steffen, K.: Cloud Radiative Forcing at Summit, Greenland, J. Climate, 28,
6267–6280, https://doi.org/10.1175/JCLI-D-15-0076.1, 2015.
Miller, N. B., Shupe, M. D., Cox, C. J., Noone, D., Persson, P. O. G., and
Steffen, K.: Surface energy budget responses to radiative forcing at Summit,
Greenland, The Cryosphere, 11, 497–516,
https://doi.org/10.5194/tc-11-497-2017, 2017.
Moran, K. P., Martner, B. E., Post, M., Kropfli, R. A., Welsh, D. C., and
Widener, K. B.: An unattended cloud-profiling radar for use in climate
research, B. Am. Meteorol. Soc., 79, 443–455,
https://doi.org/10.1175/1520-0477(1998)079<0443:AUCPRF>2.0.CO;2, 1998.
NASA LP DAAC: MODIS/Aqua Land Surface Temperature and Emissivity 5-Minute L2
Swath 1 km V006, NASA EOSDIS Land Processes DAAC, USGS Earth Resources
Observation and Science (EROS) Center, Sioux Falls, South Dakota
(https://lpdaac.usgs.gov), https://doi.org/10.5067/MODIS/MYD11_L2.006 (last
access: 3 February 2017), 2014.
Navari, M., Margulis, S. A., Bateni, S. M., Tedesco, M., Alexander, P., and
Fettweis, X.: Feasibility of improving a priori regional climate model
estimates of Greenland ice sheet surface mass loss through assimilation of
measured ice surface temperatures, The Cryosphere, 10, 103–120,
https://doi.org/10.5194/tc-10-103-2016, 2016.
Østby, T. I., Schuler, T. V., and Westermann, S.: Severe cloud
contamination of MODIS Land Surface Temperatures over an Arctic ice cap,
Svalbard, Remote Sens. Environ., 142, 95–102, https://doi.org/10.1016/j.rse.2013.11.005,
2014.
Overland, J. E. and Guest, P. S.: The Arctic snow and air temperature budget
over sea ice during winter, J. Geophys.Res.-Oceans (1978–2012), 96,
4651–4662, https://doi.org/10.1029/90JC02264, 1991.
Phillpot, H. R. and Zillman, J. W.: The surface temperature inversion over
the Antarctic continent, J. Geophys. Res., 75, 4161–4169,
https://doi.org/10.1029/JC075i021p04161, 1970.
Polashenski, C. M., Dibb, J. E., Flanner, M. G., Chen, J. Y., Courville, Z.
R., Lai, A. M., Schauer, J. J., Shafer, M. M., and Bergin, M.: Neither dust
nor black carbon causing apparent albedo decline in Greenland's dry snow
zone: Implications for MODIS C5 surface reflectance, Geophys. Res. Lett., 42,
9319–9327, https://doi.org/10.1002/2015GL065912, 2015.
Rae, J. G. L., Aðalgeirsdóttir, G., Edwards, T. L., Fettweis, X.,
Gregory, J. M., Hewitt, H. T., Lowe, J. A., Lucas-Picher, P., Mottram, R. H.,
Payne, A. J., Ridley, J. K., Shannon, S. R., van de Berg, W. J., van de Wal,
R. S. W., and van den Broeke, M. R.: Greenland ice sheet surface mass
balance: evaluating simulations and making projections with regional climate
models, The Cryosphere, 6, 1275–1294,
https://doi.org/10.5194/tc-6-1275-2012, 2012.
Reeh, N.: Parameterization of melt rate and surface temperature on the
Greenland ice sheet, Polarforschung, 59, 113–128, 1989.
Reeves Eyre, J. E. J. and Zeng, X.: Evaluation of Greenland near surface air
temperature datasets, The Cryosphere, 11, 1591–1605,
https://doi.org/10.5194/tc-11-1591-2017, 2017.
Riggs, G. A., Hall, D. K., and Román, M. O.: Overview of NASA's MODIS and
Visible Infrared Imaging Radiometer Suite (VIIRS) snow-cover Earth System
Data Records, Earth Syst. Sci. Data, 9, 765–777,
https://doi.org/10.5194/essd-9-765-2017, 2017.
Rignot, E., Velicogna, I., van den Broeke, M. R., Monaghan, A., and Lenaerts,
J. T.: Acceleration of the contribution of the Greenland and Antarctic ice
sheets to sea level rise, Geophys. Res. Lett., 38, L05503,
https://doi.org/10.1029/2011GL046583, 2011.
Shamir, E. and Georgakakos, K. P.: MODIS Land Surface Temperature as an index
of surface air temperature for operational snowpack estimation, Remote Sens.
Environ., 152, 83–98, https://doi.org/10.1016/j.rse.2014.06.001, 2014.
Shuman, C. A., Steffen, K., Box, J. E., and Stearns, C. R.: A dozen years of
temperature observations at the Summit: Central Greenland automatic weather
stations 1987–99, J. Appl. Meteorol., 40, 741–752,
https://doi.org/10.1175/1520-0450(2001)040<0741:ADYOTO>2.0.CO;2, 2001.
Shuman, C. A., Hall, D. K., DiGirolamo, N. E., Mefford, T. K., and
Schnaubelt, M. J.: Comparison of near-surface air temperatures and MODIS
ice-surface temperatures at Summit, Greenland (2008–13), J. Appl. Meteorol.
Climatol., 53, 2171–2180, https://doi.org/10.1175/JAMC-D-14-0023.1, 2014.
Steffen, K. and Box, J.: Surface climatology of the Greenland ice sheet:
Greenland Climate Network 1995–1999, J. Geophys. Res.-Atmos., 106,
33951–33964, https://doi.org/10.1029/2001JD900161, 2001.
Steffen, K., Box, J., and Abdalati, W.: Greenland climate network: GC-Net, US
Army Cold Regions Research and Engineering (CRREL), CRREL Special Report,
98–103, 1996.
Stocker, T. F.: Climate change 2013: the physical science basis: Working
Group I contribution to the Fifth assessment report of the Intergovernmental
Panel on Climate Change, Cambridge University Press, 2014.
Sverdrup, H. U.: Scientific work of the Maud Expedition, 1922–1925, The
Scientific Monthly, 22, 400–410, 1926.
Van As, D., Fausto, R. S., and PROMICE Project Team: Programme for monitoring
of the Greenland Ice Sheet (PROMICE): first temperature and ablation records,
in: Review of survey activities 2010, edited by: Bennike, O., Garde, A. A.,
and Watt, W. S., GEUS, Copenhagen, 73–76 (Geological Survey of Denmark and
Greenland Bulletin 23), 2011.
Van Den Broeke, M., Reijmer, C., Van As, D., and Boot, W.: Daily cycle of the
surface energy balance in Antarctica and the influence of clouds, Int. J.
Climatol., 26, 1587–1605, https://doi.org/10.1002/joc.1323, 2006.
van den Broeke, M. R., Enderlin, E. M., Howat, I. M., Kuipers Munneke, P.,
Noël, B. P. Y., van de Berg, W. J., van Meijgaard, E., and Wouters, B.: On
the recent contribution of the Greenland ice sheet to sea level change, The
Cryosphere, 10, 1933–1946, https://doi.org/10.5194/tc-10-1933-2016, 2016.
Van Lipzig, N. P. M., Van Meijgaard, E., and Oerlemans, J.: The effect of
temporal variations in the surface mass balance and temperature-inversion
strength on the interpretation of ice-core signals, J. Glaciol., 48,
611–621, https://doi.org/10.3189/172756502781831106, 2002.
Vernon, C. L., Bamber, J. L., Box, J. E., van den Broeke, M. R., Fettweis,
X., Hanna, E., and Huybrechts, P.: Surface mass balance model intercomparison
for the Greenland ice sheet, The Cryosphere, 7, 599–614,
https://doi.org/10.5194/tc-7-599-2013, 2013.
Waddington, E. D. and Morse, D. L.: Spatial variations of local climate at
Taylor Dome, Antarctica: Implications for paleoclimate from ice cores, Ann.
Glaciol., 20, 219–225, https://doi.org/10.3189/172756494794587014, 1994.
Wan, Z: MODIS Land-Surface Temperature Algorithm Theoretical Basis Document
(LST ATBD) Version 3.3, available at:
https://modis.gsfc.nasa.gov/data/atbd/atbd_mod11.pdf (last access: 4
March 2018), 1999.
Wan, Z.: New refinements and validation of the MODIS land-surface
temperature/emissivity products, Remote Sens. Environ., 112, 59–74,
https://doi.org/10.1016/j.rse.2006.06.026, 2008.
Wan, Z.: New refinements and validation of the collection-6 MODIS
land-surface temperature/emissivity product, Remote Sens. Environ., 140,
36–45, https://doi.org/10.1016/j.rse.2013.08.027, 2014.
Wan, Z. and Dozier, J.: A generalized split-window algorithm for retrieving
land-surface temperature from space, IEEE T. Geosci. Remote, 34, 892–905,
https://doi.org/10.1109/36.508406, 1996.
Warren, S. G. and Brandt, R. E.: Optical constants of ice from the
ultraviolet to the microwave: A revised compilation, J. Geophys. Res., 113,
D14220 https://doi.org/10.1029/2007jd009744, 2008.
Wenny, B., Xiong, X., and Madhavan, S.: Evaluation of Terra and Aqua MODIS
thermal emissive band calibration consistency, in: SPIE Remote Sensing,
853317–853317, International Society for Optics and Photonics,
https://doi.org/10.1117/12.974230, 2012.
Westermann, S., Langer, M., and Boike, J.: Systematic bias of average
winter-time land surface temperatures inferred from MODIS at a site on
Svalbard, Norway, Remote Sens. Environ., 118, 162–167,
https://doi.org/10.1016/j.rse.2011.10.025, 2012.
Williamson, S. N., Hik, D. S., Gamon, J. A., Jarosch, A. H., Anslow, F. S.,
Clarke, G. K., and Rupp, T. S.: Spring and summer monthly MODIS LST is
inherently biased compared to air temperature in snow covered sub-Arctic
mountains, Remote Sens. Environ., 189, 14–24, https://doi.org/10.1016/j.rse.2016.11.009,
2017.
Yu, Y., Rothrock, D. A., and Lindsay, R. W.: Accuracy of sea ice temperature
derived from the advanced very high resolution radiometer, J. Geophys.
Res.-Oceans (1978–2012), 100, 4525–4532, https://doi.org/10.1029/94JC02244, 1995.
Xiong, X., Wu, A.,Wenny, B. N., Madhavan, S., Wang, Z., Li, Y., Chen, N.,
Barnes, W. L., and Salomonson, V. V.: Terra and Aqua MODIS Thermal Emissive
Bands On-Orbit Calibration and Performance, IEEE T. Geosci. Remote, 53,
5709–5721, https://doi.org/10.1109/TGRS.2015.2428198, 2015.
Short summary
In our studies of surface temperature in Greenland, we found that there can be differences between the temperature of the snow surface and the air directly above, depending on wind speed and incoming solar radiation. We also found that temperature measurements of the snow surface from remote sensing instruments may be more accurate than previously thought. Our results are relevant to studies of climate change in the remote sensing community and in studies of the atmospheric boundary layer.
In our studies of surface temperature in Greenland, we found that there can be differences...
